Please use this identifier to cite or link to this item: https://doi.org/10.1109/CEC.2012.6252968
Title: A coevolution genetic programming method to evolve scheduling policies for dynamic multi-objective job shop scheduling problems
Authors: Nguyen, S.
Zhang, M.
Johnston, M.
Tan, K.C. 
Issue Date: 2012
Citation: Nguyen, S.,Zhang, M.,Johnston, M.,Tan, K.C. (2012). A coevolution genetic programming method to evolve scheduling policies for dynamic multi-objective job shop scheduling problems. 2012 IEEE Congress on Evolutionary Computation, CEC 2012 : -. ScholarBank@NUS Repository. https://doi.org/10.1109/CEC.2012.6252968
Abstract: A scheduling policy (SP) strongly influences the performance of a manufacturing system. However, the design of an effective SP is complicated and time-consuming due to the complexity of each scheduling decision as well as the interactions between these decisions. This paper proposes novel multi-objective genetic programming based hyper-heuristic methods for automatic design of SPs including dispatching rules (DRs) and due-date assignment rules (DDARs) in job shop environments. The experimental results show that the evolved Pareto front contains effective SPs that can dominate various SPs from combinations of existing DRs with dynamic and regression-based DDARs. The evolved SPs also show promising performance on unseen simulation scenarios with different shop settings. On the other hand, the proposed Diversified Multi-Objective Cooperative Coevolution (DMOCC) method can effectively evolve Pareto fronts of SPs compared to NSGA-II and SPEA2 while the uniformity of SPs obtained by DMOCC is better than those evolved by NSGA-II and SPEA2. © 2012 IEEE.
Source Title: 2012 IEEE Congress on Evolutionary Computation, CEC 2012
URI: http://scholarbank.nus.edu.sg/handle/10635/68733
ISBN: 9781467315098
DOI: 10.1109/CEC.2012.6252968
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